👋 About Me

Hi there, 👋 I am Jiaxiang Yi, a Ph.D. candidate at the Department of Mechanical Engineering, Delft University of Technology (TU Delft).

My research focuses on developing uncertainty quantification and multi-fidelity modeling methods for machine learning models. I apply these methods to problems in data-driven mechanics, particularly in constitutive modeling and design and analysis of recycled polymers. Before joining TU Delft, I earned both my Bachelor’s and Master’s degrees from Huazhong University of Science and Technology (HUST) in China. During my Master’s program, my research centered on developing active-learning surrogate models for reliability analysis and Bayesian optimization for engineering design.

🎯 Research Vision

My long-term goal is to develop trustworthy and interpretable machine learning models that feature principled uncertainty quantification. Building upon theoretically founded uncertainty quantification approaches, I aim to advance my research to address real-world problems in engineering and science — including, but not limited to, the autonomous and reliable design of complex materials ranging from recycled polymers to high-performance composites.